The mining industry generates billions of tonnes of waste annually, which is often stored in tailings storage facilities (TSF). This waste is generated from the extraction of ore from surface or underground mines, as well as from metallurgical processing and low-grade stockpiles. TSF can have significant environmental impacts, as they can cause acid mine drainage resulting in the leaching and transport of heavy metals into ground and surface waters. With increasing demand for critical raw material, recent studies have shown that the valorisation of mine waste can be a potential secondary source of critical raw materials. The valorisation of mine waste is possible when the waste is accurately characterised.A novel method that uses multispectral satellite remote sensing and machine learning to estimate the mineral resource in a defunct TSF in the Witwatersrand Basin, South Africa is proposed in this research. Four machine learning models: 1) random forest (RF); 2) adaptive boosting (AB); 3) extra trees (ET); and 4) k-nearest neighbours are developed using supervised machine learning. The models are trained using training data acquired from a TSF with known gold concentration located 3 kilometres from the TSF and deployed on the TSF to predict the gold grades. The results of the machine learning model predictions indicates that machine learning models had high performances for predicting gold grades in the TSF. The AB, RF and ET, models performed best. Their performances were evaluated using the coefficient of determination (R2) value. The R2 values for the machine learning models were 0.95, 0.92, 0.87 and 0.70 for AB, ET, RF and kNN respectively. The mean gold grade predicted was 0.44 g/t by all machine learning models. This was compared to a 2D surficial geostatistical model which estimated 0.35g/t gold in the TSF using ordinary kriging and a 2D vertically averaged geostatistical model with an estimated 0.4 g/t mean gold grade. The short-wave infrared (SWIR) - band 11 at a 20 m spatial resolution had the highest correlation with the reflectance of gold in the TSF. This study demonstrated the value of leveraging multi-spectral remote sensing data and machine learning to perform mineral resource estimation in defunct TSF.

Vegetation establishment is one of the major rehabilitation methods that are used to stabilize, cover, to minimize, mitigate or remove the contaminants from tailings storage facilities (TSF’s). Phytostabilization is a useful mechanism by which plants limit the contamination of natural systems with toxic elements. For successful occurrence of phytostabilization on mine tailings, it is vital to establish plant species that can survive the hostile conditions of the substrate. Major problems encountered with vegetation covers is the lack of natural soil properties e.g. soil structure, organic carbon and also hostile chemical conditions. Only a few species are tolerant to the different negative properties of the tailings. The main aim of this project is to identify plant species that can be used for vegetative rehabilitation of nine different types of tailings material including gypsum, gold, platinum, kimberlite, coal, fluorspar and andalusite tailings. The ability of 28 different plant species to survive in the tailings was assessed by statistically calculating the growth potential of the species and summarizing the data in graphs and an index table that calculates a specific merit value for each of the tailings-species combinations. The various plant stress factors that the species exhibited were also documented. Finally, the results were correlated with a soil physical and -chemical baseline study of the tailings to provide insight into successes and failures of certain species. The final results identified various successful tailings-species combinations, as well as failures. The index table proved to be a useful tool to identify suitable species for establishment on various tailings. The baseline study of the different tailings could be used to explain why certain species could be established successfully, as well as the reason why some species did not survive. / M (Environmental Sciences), North-West University, Potchefstroom Campus, 2015

Vegetation establishment is one of the major rehabilitation methods that are used to stabilize, cover, to minimize, mitigate or remove the contaminants from tailings storage facilities (TSF’s). Phytostabilization is a useful mechanism by which plants limit the contamination of natural systems with toxic elements. For successful occurrence of phytostabilization on mine tailings, it is vital to establish plant species that can survive the hostile conditions of the substrate. Major problems encountered with vegetation covers is the lack of natural soil properties e.g. soil structure, organic carbon and also hostile chemical conditions. Only a few species are tolerant to the different negative properties of the tailings. The main aim of this project is to identify plant species that can be used for vegetative rehabilitation of nine different types of tailings material including gypsum, gold, platinum, kimberlite, coal, fluorspar and andalusite tailings. The ability of 28 different plant species to survive in the tailings was assessed by statistically calculating the growth potential of the species and summarizing the data in graphs and an index table that calculates a specific merit value for each of the tailings-species combinations. The various plant stress factors that the species exhibited were also documented. Finally, the results were correlated with a soil physical and -chemical baseline study of the tailings to provide insight into successes and failures of certain species. The final results identified various successful tailings-species combinations, as well as failures. The index table proved to be a useful tool to identify suitable species for establishment on various tailings. The baseline study of the different tailings could be used to explain why certain species could be established successfully, as well as the reason why some species did not survive. / M (Environmental Sciences), North-West University, Potchefstroom Campus, 2015

MESMEG / Department of Mining and Environmental Geology / This study entails geochemical and mineralogical characterization of gold tailings of Nestor Mine and Glynn’s Lydenberg Mine of the Sabie-Pilgrim’s Rest goldfields. A total of 35 samples were collected and were analysed for chemical composition (XRF and ICP-MS), mineralogical composition (XRD). In addition, acid-base accounting (ABA) techniques had been conducted to predict the potential for acid mine drainage. Seepage from Nestor tailings dump and water samples from the adjacent Sabie River were also collected and analysed by means of inductively coupled plasma mass spectrometry (ICP-MS) and immediate constituent (IC) -analytical techniques. The study revealed that Sabie-pilgrim’s rest goldfield is characterized by both acid generating and non-acid producing tailings, and this is attributed to variations in the mineralogy of source rocks. Gold occurred within the Black Reef Quartzite Formation in the Nestor Mine and within the Malmani Dolomite in the case of Glynn’s Lydenburg Mine. Mineralogy and bulk geochemical analyses performed in this study showed a clear variation in the chemistry of Nestor Mine and Glynn’s Lydenburg Mine tailings. Predominant oxides in Nestor mine tailings samples are SiO2 (ranging from 66.7-91.25 wt. %; followed by Fe2O3 and Al2O3 (in range of 0.82-15.63 wt. %; 3.21-12.50 wt. % respectively); TiO2 (0.18-10.18 wt. %) and CaO (0.005-3.2 wt. %). Also occurring in small amounts is CaO (0.005-3.2 wt. %), K2O (0.51-2.27 wt. %), MgO (0.005-1.46 wt. %), P2O5 (0.029-0.248), Cr2O3 (0.013-0.042 wt. %) and Na2O (0.005-0.05 wt. %). The samples also contain significant concentrations of As (137-1599 ppm), Cu (34-571 ppm), Cr (43-273 ppm), Pb (12-276 ppm), Ni (16-157 ppm), V (29-255 ppm), and Zn 7-485 ppm). In the Glynn’s Lydenburg Mine tailings SiO2 is also the most dominant oxide ranging between 47.95 and 65.89 w%; followed by Al2O3 (4.31 to 16.19 wt. %), Fe2O3 (8.48 to 11.70 wt %), CaO (2.18 to 7.10 wt. %), MgO (2.74 to 4.7 wt. %). Occurring in small amounts is K2O (1.12-1.70 wt. %), MnO (0.089-0.175 wt. %), P2O5 (0.058-0.144 wt. %) and Cr2O3 (0.015-0.027 wt. %). Arsenic (As), is also occurring in significant amounts (807-2502 ppm), followed by Cr (117-238 ppm), Cu (10-104 ppm), V (56-235 ppm), Ni (45-132 ppm), Pb (13-63 ppm) and Zn (90-240 ppm). Nestor Mine tailings associated with Black Reef Formation mineralization have net neutralizing potential (NPR) <2, hence more likely to generate acid; and their acid potential (AP) ranges 1.56 to 140.31 CaCO3/ton and neutralizing potential (NP) range from -57.75 to -0.3 CaCO3/ton. Glynn’s Lydenburg Mine tailings dump which is vi associated with dolomite mineralization, however, was not leaching acid. Based on acid-base accounting results, these tailings have more neutralizing potential (ranging between 57.6 and 207.88 CaCO3/ton) than acid potential (ranging between 7.5 and 72.1 CaCO3/ton); and their NPR>2, hence unlikely to produce acid. This is confirmed by paste pH which was in the ranges between 7.35 and 8.17. Tailings eroded from Nestor Mine tailings dump were also found to be characterized by high content of metals and oxides, namely, As, Cu, Ni, Pb, V, and Zn with SiO2, Fe2O3 and TiO2. The tailings were observed eroded into the Sabie River where AMD related precipitate (yellow boy) was also observed, indicating further oxidation downstream. Field observations, onsite analyses of water samples and laboratory results revealed that Nestor Mine tailings storage facility discharges acid mine drainage with considerable amounts of Al, As, Cu, Fe, Mn, Zn and SO4 and very low pH exceeding the limit as per South African water quality standards. High concentrations of these metals have toxicity potential on plants, animals and humans. Upon exposure to oxygen and water, tailings from Nestor Mine are more likely to generate acid mine drainage that can cause detrimental effect to the environment and the surrounding communities. Potential pollutants are Fe, Mn, Al, As, Cr, Cu, Ni and Pb. Tailings from Glynn’s Lydenberg showed no potential for acid mine drainage formation. / NRF